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Article

Transcriptomic Analysis of Sunflower (Helianthus annuus) Roots Resistance to Orobanche cumana at the Seedling Stage

by
Qixiu Huang
1,†,
Zhonghua Lei
1,2,†,
Lijun Xiang
1,*,
Wangfeng Zhang
2,*,
Li Zhang
1 and
Yan Gao
1
1
Institute of Economic Crops, Xinjiang Academy of Agricultural Sciences, Urumqi 830091, China
2
Key Laboratory of Oasis Eco-Agriculture, Xinjiang Production and Construction Corps, College of Agronomy, Shihezi University, Shihezi 832003, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Horticulturae 2022, 8(8), 701; https://doi.org/10.3390/horticulturae8080701
Submission received: 20 June 2022 / Revised: 29 July 2022 / Accepted: 1 August 2022 / Published: 3 August 2022
(This article belongs to the Section Genetics, Genomics, Breeding, and Biotechnology (G2B2))
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Abstract

:
Orobanche cumana is a root alloparasitic plant that drastically reduces sunflower (Helianthus annuus) production. In this study, transcriptomic changes of O. cumana-resistant (HZ2399) and O. cumana-sensitive (SQ25) sunflower seedlings were investigated at six time points (0–72 h) following O. cumana infection. The process of resistance to O. cumana was similar in HZ2399 and SQ25 seedlings, however, significantly higher regulatory activity was observed in the resistant plants. In HZ2399, most of the 54 upregulated genes were involved in phenylpropanoid biosynthesis, plant–pathogen interaction, and plant hormone signal transduction pathways. These genes were mainly associated with antioxidant responses, responses to stress, stimulation responses, and metabolic processes. The expression level of the three most significantly upregulated genes in HZ2399 (4CL2, EDS1, and TGA3) was significantly higher than that of SQ25, suggesting that they may be the main causes of O. cumana immunity in HZ2399. It is hypothesized that sunflower resistance to O. cumana parasitism is dependent on salicylic acid (SA), a disease resistance protein (TIR-NBS-LRR class) family (RPS4), and EDS1. The results of this study contribute to elucidating the mechanism of O. cumana resistance in sunflower and for the molecular breeding of O. cumana resistance.

1. Introduction

Sunflower (Helianthus annuus) is an important cash crop grown on a large scale worldwide and may be used for soil improvement because of its adaptability, drought tolerance, salinity tolerance, and infertility. It is also used as a biofuel, green manure, and raw material for health products. Sunflower seeds are a major source of edible oil worldwide, and seed consumption is increasing annually.
Orobanche cumana is an obligate root parasitic plant of sunflowers, tomatoes, aubergines, and tobacco. Sunflower parasitized by O. cumana has slow growth, drastically reduced yields, and severely affected oil and protein content [1]. At present, the prevention and control measures for O. cumana mainly include chemical control [2], biological control [3], and induced resistance [4]. It is difficult to predict the best time to control O. cumana since it tends to form a seed bank in the soil with long survival periods. Current prevention and treatment measures do not address the root cause of parasitism, and the effect is not very satisfactory. For example, chemical control is environmentally unfriendly and prone to drug damage, physical control is ineffective and time-consuming, biological control is costly, and resistance induction is not effective in field trials. Hence, breeding resistant varieties was considered economical, environmentally friendly, and an effective control method for a period [5], however, this resistance was quickly overcome [6]. First of all, some studies reported that the genes and proteins of O. cumana will change during the intercropping between O. cumana and sunflower, and O. cumana will secrete proteins involved in the degradation and modification of plant cell walls, producing proteins with expansin structural domains that facilitate cell loosening [7]. Secondly, it can overcome the hypothesis that the discriminating hosts of high-level physiological minor species of O. cumana and low-ranking discriminating hosts produce changes in genes and proteins when they encounter low-ranking physiological minor species, for example, the hypothesis that there is a gene-to-gene pattern during the mutualism between sunflower and O. cumana [8]. The virulence factors released by plant pathogens mutate to adapt to the species, resulting in the formation of new physiological minor species, and the plant evolves new R genes to counteract high-level physiological minor species. Thus, R genes in sunflower that can resist high levels of physiological minor species parasitism must also have undergone significant expression changes to counteract the virulence factors released by O. cumana. Therefore, it is essential to understand the defense mechanism of sunflower against O. cumana parasitism in order to provide a scientific basis for the development of new germplasm resources with durable resistance and effective O. cumana control methods.
Studies of plant resistance to exotic attacks mainly focus on resistance against microorganisms, insects, and herbivores. Plants have evolved a mechanism to detect and resist attacks through a series of immune defense responses. Plant immune receptors are also known as pattern recognition receptors (PRRs) which detect highly conserved microbial structures such as microbial-associated molecular patterns (MAMPs) involving proteins such as bacterial flagellin or fungal chitin [9,10]. PRR activation triggers a range of typical defense responses, including rapid production of reactive oxygen species (ROS) burst; elevated levels of the stress-related phytohormone, ethylene; secondary metabolites including sugar, plant antitoxins, and lignin; induction of characteristic marker genes (R genes) [11,12]; and signaling via the phytohormones salicylic acid (SA) and jasmonic acid (JA) [13]. Plants exhibit complex defense responses to resist or limit the growth and spread of pathogens. Communication mechanisms between plants and parasitic plants are less studied than those between plants and microorganisms [14]. However, plants recognize parasitic plants in a similar manner to the perception of microbial pathogens [15]. In general, host resistance involves an induced defense mechanism against pathogenic bacteria or parasitic plant attack [7]. Plants may trigger innate immunity by recognizing conserved pathogen-associated molecular patterns (PAMPs) [16]. The parasitic plant/pathogen uses effectors to manipulate the host’s basal defenses, while the host plant has evolved specific resistance proteins to recognize specific effectors, which are known as gene-to-gene interactions [17]. In addition, plants have other primarily intracellular pathogen detection systems that function and rely on a range of resistance proteins [18]. Often, these microbial pathogen resistance proteins trigger strong hypersensitive response symptoms comparable to those induced by parasitic plants.
Breeders have used naturally resistant sunflower germplasm to create many resistant varieties. HZ2399 is a sunflower hybrid resistant to O. cumana bred in 2019 by the Institute of Economic Crops, Xinjiang Academy of Agricultural Sciences, and its parent is a self-replicating line immune to O. cumana as determined by multi-point identification over many years. Resistant sunflower genotypes respond earlier to attack by O. cumana compared to sensitive genotypes through induction of SA and JA signaling pathways, while ethylene is not involved [19]. A leucine-rich repeat (LRR) receptor-like kinase enhances O. cumana resistance in sunflower [20]. It is suggested that the plant growth inhibitor of gibberellin (GA) biosynthesis, prohexadione-calcium (PHDC), reduces O. cumana infectivity in sunflower by inducing host resistance through responses by free phenols [21]. Direct evidence of induced resistance in sunflower was shown by treating seeds with 40 ppm of benzo (1,2,3) thiadiazole-7-carbonsulfate S-methyl ester (BTH) for 36 h, which completely prevented infection by O. cumana, with the accumulation of tyrosinase in the roots and stems showing that induced resistance is not restricted to viral, bacterial, and fungal diseases [4]. However, it will take a long time for chemically induced resistance to become the future direction, and there are still many problems to be solved on the road to practical application. For example, compounds that stimulate the induction of resistance in theoretical studies do not necessarily have a control effect, and the mechanism of resistance induction needs to be more deeply explored. In practice, it has also been found that the control effect in indoor or plot trials is very small, and even if there is an effect, it does not mean that it can be applied in production. In addition, it is debatable whether the elicitor has an effect on beneficial microorganisms in plants and whether it has an effect on farmland and other biological environments during application.
Completion of the sunflower genome [22] has provided a solid foundation for sunflower genetic research. Three weeks after inoculation, transcriptome sequencing revealed upregulation of carbohydrate metabolic processes during the interaction between sunflower and O. cumana, suggesting that sunflower consumes a lot of energy, which may contribute to O. cumana growth. It is also suggested that O. cumana inhibits nutrient uptake and/or transport in susceptible sunflowers by expressing its own corresponding transporter and allowing more efficient nutrient flow to O. cumana [7]. The most representative classes of induced proteins from proteomes of resistant and sensitive sunflower following interaction with O. cumana were as follows: general function, post-translational modifications, energy production and conversion, carbohydrate transport and metabolism, and signal transduction mechanisms [23]. Resistance interactions are characterized by alterations in defense-related proteins involved in parasite recognition, accumulation of pathogenesis-related proteins, lignin biosynthesis, and detoxification of toxic metabolites following inoculation. Sensitive interactions were characterized by reduced protein abundance involved in the biosynthesis and signaling of plant growth regulators, growth hormone, GA, oleuropein lactone, and ethylene.
The mechanisms of host resistance to parasitic plants have been extensively studied [24], however, studies on the allopatric weed O. cumana are incomplete and imprecise. Previous studies of sunflower and O. cumana intercropping have mainly focused on the middle and late infection stages, i.e., when nodules have been produced or when O. cumana has sprouted from the soil [7,23]. Our laboratory and field studies revealed parasitic nodules in sunflower after 15 days of intercropping with O. cumana, which is like the onset of many fungal diseases. Usually, fungal diseases are usually studied at time points such as 4, 12, 24, and 48 h after inoculation. It is suggested that O. cumana parasiticum, like many fungal diseases, undergoes transcriptional expression changes at an earlier time to resist pathogen or parasitic plant attack. In our previous study, it was observed by microscopy that O. cumana seeds were closely associated with sunflower roots at time points such as 4 and 16 h after O. cumana–sunflower intercropping, and in addition to this, root secretions from sunflower seedlings were found to induce O. cumana seed germination within 2 days. This suggests that early transcriptional regulatory responses play a critical role in the development of O. cumana resistance in sunflowers. In this study, resistant and susceptible sunflower plants were used for roots transcriptomic analysis from the early intercropping stage with O. cumana to reveal the key regulatory factors and mechanisms of O. cumana resistance in sunflower.

2. Materials and Methods

2.1. Experimental Materials

The sunflower cultivar resistant to O. cumana is the parent of the O. cumana-resistant hybrid HZ2399 (sterile line), while the O. cumana-sensitive cultivar is SQ25. Both cultivars were expanded and preserved by the Sunflower Group of the Institute of Economic Crops, Xinjiang Academy of Agricultural Sciences, Xinjiang, 830091, China. The O. cumana is a physiological minor “F” grade, which was collected and preserved by the same institute.

2.2. Sampling Method

The field-collected O. cumana seeds were sterilized with 75% alcohol three times for five min at a time, rinsed twice with sterile water, and dried naturally at room temperature. Sunflower seeds were disinfected three times with 75% alcohol, rinsed three times with sterile water, and set aside. One edge of a 10 cm × 10 cm piece of sterile filter paper was moistened and the sunflower seeds were placed in order, approximately 3–4 sunflower seeds per sheet. Each sheet of filter paper with seeds was rolled up and placed vertically in a germination box which was filled with sterile water approximately 2 cm deep. Rolled-up filter paper and seeds were separated by leather strips, kept in the dark for 1 d at 23 to 28 °C, and then incubated in a 16 h light–8 h dark cycle at 23 to 28 °C and 1000 μmol photons·m−2·s–1 light intensity. The water in the germination box was monitored daily and replenished to a depth of 2 cm. Inoculation of O. cumana seeds was carried out after approximately 5–7 days when the two cotyledons had spread out and the main root was longer than 10 cm. The filter paper was unrolled, and an equal mass of O. cumana seeds was scattered evenly near the main roots; rolled back; and incubated for 0, 4, 16, 24, 48, and 72 h. The root samples were removed with sterilized scissors, washed in sterile water to remove impurities, and blotted dry with filter paper. Samples were wrapped in tin foil, placed in liquid nitrogen, numbered (Table 1), and sent for transcriptomic analysis.

2.3. Library Preparation for Transcriptome Sequencing

Roots were ground in liquid nitrogen, and RNA was extracted using TRIzol reagent (Thermo Fisher Scientific, Shanghai, China) according to the manufacturer’s instructions. RNA concentration was determined using a Nanodrop 1000 (Thermo Fisher Scientific, Shanghai, China). A total of 1 μg RNA per sample was used as the input material for RNA sample preparations. Briefly, mRNA was purified from total RNA using poly T oligo-attached magnetic beads. RNA integrity was assessed using the RNA 6000 Nano kit of the 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA, USA). Fragmentation was performed at 94 °C for 15 min using 500 ng RNA, 10 µM random hexameric primers, and 5× First Strand Synthesis reaction buffer (New England Biolabs, Beijing, China) in a total volume of 100 µL. First-strand cDNA was synthesized using random hexameric primers and M-MuLV reverse transcriptase (RNase H-, Vivantis). Second-strand cDNA synthesis was subsequently performed using DNA Polymerase I and RNase H. Remaining overhangs were converted into blunt ends via exonuclease/polymerase activities. After adenylation of the 3′ ends of DNA fragments, an adaptor with a hairpin loop structure was ligated in preparation for hybridization. Library cDNA fragments of preferentially 370–420 bp in length were purified with the AMPure XP Capabilities (Beckman Coulter, Beverly, CA, USA). PCR was performed using Phusion high-fidelity DNA polymerase (Thermo Fisher Scientific, Shanghai, China), universal PCR primers, and Index (X) primer. PCR products were purified on a Biomek 4000 Laboratory Automation Workstation (Beckman Coulter, Beverly, CA, USA) and library quality was assessed using an Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA, USA).

2.4. Clustering and Sequencing

Clustering of the index-coded samples was performed on a cBot cluster generation system using a TruSeq PE cluster kit v3-cBot-HS (Illumina, San Diego, CA, USA) according to the manufacturer’s instructions. After cluster generation, the library preparations were sequenced on an Illumina Novaseq platform and 150 bp paired-end reads were generated (NovogeneBioinformatics Technology, Beijing, China).

2.5. Quality Control and Mapping Reads to the Reference Genome

Raw data (raw reads) of fastq format were first processed using an in-house Perl script. Clean data were obtained by removing reads containing adapters, reads containing poly-N, and low-quality reads from raw data. At the same time, Q20, Q30, and GC contents of the clean data were calculated. All downstream analyses were based on high-quality, clean data. Sunflower reference genome and gene model annotation files were downloaded directly from the genome website (https://www.ncbi.nlm.nih.gov/genome/?term=txid4232[orgn]; accessed on 15 May 2021). The index of the reference genome was built using Hisat2 v2.0.5, and paired-end clean reads were aligned to the reference genome. Hisat2 was selected as the mapping tool because it can generate a database of splice junctions based on the gene model annotation file.

2.6. Quantification of Gene Expression Level

FeatureCounts v1.5.0-p3 was used to count the read numbers mapped to each gene. The fragments per kilobase of transcript sequence per million base pairs sequenced (FPKM) of each gene was calculated based on the length of the gene and read count mapped to this gene.

2.7. Differential Expression Analysis

DESeq2 R package (1.20.0) was used to analyze the differential expression of two conditions/groups (two biological replicates per condition). This program provides statistical routines for determining differential expression in digital gene expression data using a model based on a negative binomial distribution. The resulting p-values were adjusted using the Benjamini–Hochberg approach for controlling the false discovery rate. Genes with an adjusted p-value < 0.05 were assigned as differentially expressed.

2.8. GO and KEGG Enrichment Analysis of DEGs

ClusterProfiler R package was used for GO and KEGG (http://www.genome.jp/kegg/; accessed on 22 May 2021) enrichment analysis of DEGs in which gene length bias was corrected, with GO and KEGG having corrected p-values < 0.05 considered significantly enriched.

2.9. qPCR

The gene expression of 15 genes at six time points of HZ2399 and SQ25 interactions with O. cumana was examined using qPCR. The sampling method is described in the previous section. Total root RNA was extracted from all the samples using the TRIzol method. Genomic DNA was removed by DNase as stated in the manufacturer’s instructions, and reverse transcription was performed using RevertAid reverse transcriptase (Thermos Fisher Scientific, Shanghai, China) with the reaction product stored at −20 °C. A 20 µL assay volume was used for the qPCR reaction using AceQ Universal SYBR qPCR master mix (Q511-02, Vazyme Biotech, Nanjing, China): 10 µL; forward primer: 0.4 µL (10 µM); reverse primer: 0.4 µL (10 µM); ddH2O: 6.7 µL; cDNA: 2.5 µL (1 µg). Primer sequences used for the experiment are stated in Supplemental Table S1. Three replicates were used for each experiment. Data were analyzed using the 2−ΔΔCT method. The internal reference gene was ef-1α10.

3. Results

3.1. O. cumana Parasitism of Sunflower

HZ2399 is a hybrid O. cumana-resistant sunflower bred in 2019 by the Sunflower Research Institute of Xinjiang Academy of Agricultural Sciences. The parent of this hybrid is an O. cumana-immune self-incompatible line, and both the hybrid and the parent have O. cumana immunity in field trials and in laboratory inoculations. A field nursery in Changji showed no O. cumana parasitism in the HZ2399 parent from seedling emergence to mature harvest in the same year. The O. cumana-susceptible sunflower (SQ25) was selected from a large number of high-generation self-replicating lines. A parasitism rate of 26% was observed 30 days after sowing, and a parasitism rate of 94% was observed at maturity. The SQ25 parasitism rate rapidly increased from 30 to 60 days after sowing (Figure 1), and the rapid early-stage increases in parasitism illustrate the sensitivity of SQ25 to O. cumana. An average of 4.7 O. cumana weeds were observed per SQ25 sunflower plant 90 days after infection (Figure 2).

3.2. Analysis of Overall Sequencing Data Quality

Transcriptome sequencing was performed on HZ2399 and SQ25 root material collected after 4, 12, 24, 48, and 72 h inoculation with O. cumana seeds. No inoculation is represented by 0 h. Three biological replicates were used for each treatment. The 1.68 billion raw reads were filtered by checking the sequencing error rate and GC content distribution to generate approximately 45 million clean reads (Supplemental Table S2). The obtained reads from each sample were over 90% comparable with the sunflower genome data (Supplemental Table S3).

3.3. Sunflower Differentially Expressed Genes following Infection with O. cumana

The number of upregulated genes in HZ2399 after 4 and 12 h infection with O. cumana was 1601 and 2564, respectively, compared with 1168 and 1313 in SQ25 (Figure 3). The number of downregulated genes in HZ2399 was much higher than that in SQ25 at the early stage (after 4 and 12 h) of treatment. This indicated that the HZ2399 metabolic pathways adjust to defend against parasitism. HZ2399 and SQ25 showed many up- and downregulated genes at 24 h; however, HZ2399 differentially expressed genes (DEGs) remained stable at 48 and 72 h, while a large decrease was observed in SQ25 at 48 and 72 h. This suggested that the resistance mechanism in HZ2399 is a regular and stable process, while SQ25 induced transient and unregulated defense responses.
Correlation analysis showed good reproducibility (≥0.9) between replicates (Figure 4a), indicating that the sequencing data were reliable and could be used for subsequent analyses. There was a significant difference in the response of HZ2399 and SQ25 to O. cumana infection at the same time points and different time points. Principal component analysis revealed that the transcriptomes of the same material at different intercropping time points were closer together, while transcriptomes were farther apart between HZ2399 and SQ25 (Figure 4b). A heat map showed stage-specific variations in the number and proportion of specifically expressed genes, with distinctly different regulatory expression systems between HZ2399 and SQ25 (Figure 5). This suggested that each stage has its own independent developmental program.

3.4. Gene Expression Patterns and Pathway Enrichment during Sunflower–O. cumana Interaction

The DEGs at six time points of HZ2399 and SQ25 intercropping with O. cumana were clustered into 20 patterns, four of which showed significant differences and were selected for analysis (Figure 6a). The number of DEGs in HZ2399 (upregulated: 521; downregulated: 669) was less than that in SQ25 (upregulated: 1987; downregulated: 1875) in profile 19 and profile 0. Meanwhile, the number of genes in HZ2399 (upregulated: 540; downregulated: 711) was greater than that in SQ25 (upregulated: 455; downregulated: 396) in profile 0 and profile 1. The genes in profile 19 showed a continuous upregulation of expression, and the Gene Ontology (GO) terms were enriched in response to oxidative stress, peroxidase activity, chitinase activity, and antioxidant activity (Figure 6b), while KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment was mainly in phenylpropanoid biosynthesis, plant–pathogen interaction, and mitogen-activated protein kinase (MAPK) signaling pathway-plant (Figure 6c). The genes in profile 18 were continuously upregulated from 0 to 16 h, remained stable at high levels between 16 and 24 h, and then decreased significantly after 24 h. There were similar GO and KEGG enrichment terms to those of profile 19 (Figure 6b,c); examples include biological processes related to antioxidant response and metabolic signaling pathways related to disease resistance (phenylpropanoid biosynthesis, plant–pathogen interaction, MAPK signaling pathway). The genes in profile 0 mainly presented a negatively regulated expression pattern from 0 to 72 h. Profile 1 genes were significantly downregulated from 0 to 24 h, and the expression level increased after 24 h, which is opposite to the expression trends of profiles 18 and 19 (Figure 6a). The genes in HZ2399 were mainly enriched in the GO terms lipid metabolic processes, methylation, and ribosome (Figure 6b) and KEGG terms glycolysis/gluconeogenesis, flavonoid biosynthesis, ribosome and starch, and sucrose metabolism pathways (Figure 6c). Meanwhile, SQ25 genes were mainly enriched in the following GO terms: peroxidase activity; antioxidant activity; oxidoreductase activity, acting on peroxide as the acceptor; and photosynthesis (Figure 6b). SQ25 genes were mainly enriched in the following KEGG terms: pentose phosphate pathway and glycolysis/gluconeogenesis pathways (Figure 6c).

3.5. Transcriptomic Differences between HZ2399 and SQ25

Many differences were observed between upregulated or downregulated transcription factors of HZ2399 and SQ25 after 4 to 72 h infection with O. cumana (Figure 7). The higher percentage of upregulated transcription factors in HZ2399 at the early stages of sunflower interaction with O. cumana indicated that this may be a key regulatory response favoring the development of HZ2399 resistance to O. cumana.
HZ2399 had 11,624 upregulated genes and 11,843 downregulated genes across the five time periods (Figure 8a), with 198 upregulated genes and 926 downregulated genes at all five time points compared with SQ25, among which transcription factors accounted for 23.7% and 20.7%, respectively (Figure 8a). The upregulated pathways at the initial stages of infection mainly included plant–pathogen interaction, phenylpropanoid biosynthesis, MAPK signaling pathway, plant hormone signal transduction, flavonoid biosynthesis, and peroxidation bio-enzyme signaling pathway according to KEGG enrichment analysis (Figure 8b). The main downregulated pathways were fatty acid metabolism, carbon metabolism, glycolysis/gluconeogenesis, and fatty acid biosynthesis (Figure 8b). Starch and sucrose metabolism and sulfur metabolism were also significantly downregulated after 24 h, while no significantly enriched pathway was found at 72 h.
The main upregulated GO terms in the initial stages of interaction were response to stress; response to oxidative stress; sulfur compound transport; cell wall; peroxidase activity; oxidoreductase activity, acting on peroxide as an acceptor; sulfur compound transmembrane transporter activity; and histidine kinase activity (Figure 8c).
Further enrichment analyses focused on phenylpropanoid biosynthesis, plant hormone signal transduction, plant–pathogen interaction pathways, and biological processes involving responses to stress and responses to oxidative stress. The annotation of 54 genes in these pathways revealed many duplications with genes in our GO term focus. Twenty-eight were transcription factors, of which five belonged to the Pkinase gene family. Most were associated with antioxidant responses, stress responses, stimulus responses, and metabolic processes (Supplemental Table S4), and a significant number of genes were associated with the regulation of cellular processes: gene expression regulation, protein modification, and signal transduction. These genes may be closely associated with the resistance to O. cumana in sunflowers.
In HZ2399, 23 genes in the plant–pathogen interaction signaling pathway were significantly upregulated at 16 and 48 h after intercropping (Figure 9), with 15 upregulated phenylpropanoid biosynthesis pathway genes at 16 h, and upregulation of 28 plant hormone signal transduction pathway genes at the early stage of intercropping (4 h and especially 16 h) and 48 h after intercropping. More than 85% of the genes were upregulated by more than 2-fold at 16 h after intercropping.

3.6. Confirmation of Key Genes

The quantitative polymerase chain reaction (qPCR) results of the 15 key genes (gene expression data are presented in Supplemental Table S5), namely PAL, 4CL2, CCR, POD10, and POD11 in phenylpropanoid biosynthesis; EDSI, PTI1, WRKY2, RBOHE, and HSP83 in the plant–pathogen interaction pathway; and JAZ, TGA3, PP2C, PYL4, and ETR1 in plant hormone signal transduction, were comparable to the DEGs from the transcriptomic results (Figure 10). Increased expression of the five genes in phenylpropanoid biosynthesis was observed at 16 and 24 h (Figure 10a), suggesting that this pathway was activated during early exposure to O. cumana seeds. 4CL2 had the most significant increase among the five genes in HZ2399, and its levels were significantly different from SQ25. This suggests that it may be the main DEG affecting sunflower resistance in the phenylpropanoid biosynthesis pathway. The similar level of CCR upregulation in HZ2399 and SQ25 at five time points suggests that this gene is a homogeneous plant systemic resistance regulator rather than a gene specifically expressed in resistant cultivars.
The upregulation of five genes in the plant–pathogen interaction pathway was greatest at 24 and 72 h (Figure 10b), indicating that this pathway may have a later regulatory effect or is regulated later than the phenylpropanoid biosynthesis pathway. EDSI and WRKY2 were the most upregulated genes in HZ2399, and their expression was significantly higher than that in SQ25, indicating that they may be the main effector genes in this pathway that confer O. cumana resistance to HZ2399. Similar HSP83 expression trends and amplitudes in HZ2399 and SQ25 suggest that it is not the main gene responsible for HZ2399 resistance in this pathway.
JAZ1, TGA3, and ETR1 expression levels in HZ2399 were higher than those in SQ25, with milder, more consistent JAZ1 upregulation in HZ2399 compared with TGA3 (Figure 10c). In the first 24 h, TGA3 expression in HZ2399 was significantly higher than that in SQ25, while the opposite trend was observed at 72 h. The ETR1 expression trend was similar in HZ2399 and SQ25, indicating that this gene produces a similar regulatory response; however, the expression level was higher in HZ2399. PYL4 was not significantly different within 48 h in HZ2399 and SQ25, but its expression in HZ2399 rapidly increased at 72 h, implying that it has a delayed regulatory effect compared to the other genes in this pathway.
An overview figure was created to facilitate the reader’s understanding of the results of this study (Figure 11). In the present study, the transcriptome sequencing of two varieties with markedly different resistance to O. cumana revealed similar biological processes in their respective resistance processes, including response to oxidative stress, peroxidase activity, antioxidant activity, and chitinase activity. In addition, similar metabolic pathways, including phenylpropanoid biosynthesis, plant–pathogen interactions, and MAPK signaling pathway-plant were activated.

4. Discussion

Sunflower root transcriptomics during the early stages of interaction with O. cumana showed that more genes were positively up- and downregulated in the resistant sunflower cultivar (HZ2399) than in the O. cumana-susceptible cultivar (SQ25). HZ2399 maintained genetic upregulation at the later stages, suggesting that the HZ2399 resistance mechanism is a regular and stable process, while SQ25 showed transient and unregulated induction in some defensive responses. Sunflower cultivars resistant to O. cumana infection showed more protein upregulation following their co-culture with O. cumana compared with sensitive cultivars which had more downregulated proteins [23]. This differs from the present study; however, there are genes/proteins that are mutually regulated, indicating that there is a complex relationship between transcriptional regulation and protein expression. In this study, more genes were up- and downregulated in the resistant cultivar after intercropping with O. cumana, showing a more positive response. The present study suggests that positive transcriptional regulation is one of the important reasons for HZ2399 resistance to O. cumana. However, the mechanism by which the transcriptome and proteome are involved in the sunflower resistance response of O. cumana remains to be elucidated.
Cluster analysis grouped the transcriptomic data into 20 spectra to identify the key regulatory points from the many different genes involved in the defense processes of HZ2399 and SQ25. Four profiles were selected for further analysis: profile 19, which is consistently upregulated; profile 18 with upregulated expression at the beginning, maintenance of high-level expression in the middle, and downregulation at the end; and profile 0 and profile 1, which have the opposite trends to profile 19 and profile 18, respectively. The gene functions in profile 19 and profile 18 were enriched in many similar processes and pathways: antioxidant response and metabolic signaling pathways related to disease resistance (phenylpropanoid biosynthesis, plant–pathogen interaction, MAPK signaling). Most plant–pathogen recognition research involves plant–microbe or plant–insect/herbivore interactions [25]; however, studies on dodder parasitic tomatoes showed that plants recognize parasitic plants in a similar way to sensing microbial pathogens [23]. Fungal resistance of sunflower to verticillium wilt involves pathways such as plant–pathogen interaction and flavonoid biosynthesis, peroxidases, and chitinases [26]. The chitinase metabolic pathway was significantly enriched to a greater degree in HZ2399 compared with SQ25 in profile 19. Peroxidases are inducible defense-related enzymes that prevent the spread of pathogens through the production of large amounts of toxic reactive oxygen and nitrogen species [27]. Defense responses were significantly enriched in HZ2399, but not in SQ25. However, both cultivars were significantly enriched in peroxidase activity when infected by O. cumana. This provided further evidence that various regulatory responses of sunflower to resist O. cumana parasitism are similar to plant resistance to pathogenic bacteria.
Carbohydrate biosynthesis was significantly enriched in the three SQ25 profiles, but not in HZ2399 (Figure 6b), suggesting that the high energy consumption in SQ25 may contribute to O. cumana growth. Meanwhile, the main sources of plant energy supply (protein transport and photosynthetic processes) were inhibited in SQ25. Glycolysis/gluconeogenesis was significantly enriched in both resistant and sensitive cultivars (profile 0). Starch and sucrose metabolism were significantly enriched in profile 19, profile 0, and profile 1 of the resistant cultivar. In general, sunflower is protected against O. cumana in the early stages of infection by the upregulation of metabolic processes such as phenylpropanoid metabolism, MAPK transcription factors, and the phytopathogenic mutualistic system and by the reduction in energy (sugar) metabolism. Each cultivar had a specific defense mechanism against O. cumana: HZ2399 showed an aggressive defense response, while the passive strategy of SQ25 involving photosynthesis inhibition and carbohydrate metabolism upregulation appeared to favor nutrient uptake by O. cumana.
The specific differences between HZ2399 and SQ25 during intercropping with O. cumana were analyzed according to gene expression differences at the same time points. The genes upregulated in HZ2399 were mainly enriched in plant–pathogen interactions, phenylpropanoid biosynthesis, MAPK signaling pathway, plant hormone signal transduction, flavonoid biosynthesis, and peroxisome pathways (Figure 7), which coincided with the results of the previous article on resisting the O. cumana parasitic process, indicating that the first four categories and MAPK biosynthesis were important responses induced by O. cumana. Secondary metabolites derived from a variety of pathways, including terpenoids, phenylpropanoids, and nitrogenous substances, enhance the ability of plants to fight invasive pathogens [28]. Phenylpropanoids are the most widely used compounds in plant parasitism and plant disease resistance research. Phenylpropanoid biosynthesis involves a vast variety of aromatic metabolites that are critically important for growth, development, and environmental adaptation in plants [29]. 4-Coumarate-CoA ligase (4CL1) is a key enzyme in phenylalanine analog synthesis that catalyzes thioester production, including lignans, flavonoids, anthocyanins, coumarins, phytochemicals, signaling molecules, and phenolics which protect plants from pathogenic bacteria and confer mechanical strength to plant tissues [30,31]. DEG and differentially expressed metabolite (DEM) correlation analyses in parasitic clover dodder revealed 23 differentially expressed miRNA–mRNA pairs responsive to Cordyline australis parasitism. The most enriched pathways were plant hormone signal transduction, ribosome, and plant–pathogen interaction pathways [32]. Chickpea infestation by Ascochyta blight resulted in highly enriched metabolic pathways (ko01100), secondary metabolite biosynthesis (ko01110), plant hormone signal transduction (ko04075), and plant–pathogen interaction (ko04626) and significantly enriched phenylpropanoid biosynthesis and the MAPK signaling pathway [33].
Analysis of the DEGs between HZ2399 and SQ25 in plant–pathogen interaction, phenylpropanoid biosynthesis, and plant hormone signal transduction revealed that 4CL2, EDSI, and TGA3 may be important genes for HZ2399′s production of immune effects against O. cumana (Figure 8). 4CL is often considered the third step in the phenylpropanoid pathway involved in the biosynthesis of monophenols [34] and the production of plant defense secondary metabolites in leaf and stem xylem tissues [35]. Many other genes in the phenylpropanoid biosynthetic pathway are induced by pathogenic bacteria in plants [36]. In this study, POD, CCR, PAL, and 4CL from the phenylpropanoid biosynthetic pathway were induced by O. cumana. However, 4CL2 expression in HZ2399 was the most significantly increased among all genes, and its level was significantly different compared with SQ25, suggesting that this gene may play a key role for HZ2399, conferring resistance to O. cumana.
EDSI expression in the plant–pathogen interaction pathway of HZ2399 was significantly higher than that in SQ25, indicating that this may be the main effector gene conferring resistance to HZ2399 in this pathway. EDS1 is the key to plant hypersensitivity and is necessary for the accumulation of the plant defense-enhancing molecule salicylic acid [37]. The schematic diagram of the plant–pathogen interaction pathway shows that EDS1 and RPS4 work together as defense proteins by triggering programmed cell death (Supplemental Figure S1). EDS1 mutations in Arabidopsis eliminate disease resistance mediated by RPS4 [38]; however, EDS1 from Arabidopsis did not cause hypersensitivity to P. parasitica infection [39]. The differences observed with the results of this study may be due to species-specific differences in defense response mechanisms between parasitic plants and hosts. This study suggested that sunflower resistance to O. cumana parasitism is dependent on RPS4-mediated resistance processes involving EDS1. Salicylic acid is a key hormone for enhancing disease resistance in plants, and the cooperation between nitric oxide, reactive oxygen intermediates, and salicylic acid contributes to establishing a hypersensitive response through the enhancement of defense signals in surrounding plant tissues [40,41]. TGA3 expression in HZ2399 was significantly higher than that of SQ25 in the first 24 h of O. cumana interaction, indicating that high EDS1 expression is conducive to salicylic acid accumulation and ultimately leads to HZ2399 immunity against O. cumana.
Upregulated genes in HZ2399 were enriched in GO terms such as response to stress, response to oxidative stress, sulfur compound transport, cell wall, and histidine kinase activity. Sulfur compound transport was significantly enriched at several time points in the resistant cultivar, with most of the upregulated genes being sulfate transporters. The important role of sulfur compounds in plant disease resistance is becoming increasingly apparent [42]. Intracellular or intercellular sulfate transport in plants is mediated by sulfate transporters [43], which undergo activation [44] and reduction [45] processes in plant cells to form cysteine, the first organic molecule to carry reduced sulfur central for the biosynthesis of sulfur-containing defense compounds in plants [46]. The present study suggests that the sulfate transporter plays a positive role in enhancing sunflower resistance against O. cumana, and the increased sulfate transport implies that cysteine accumulates at a later stage.
In this study, a sunflower–O. cumana intercropping system was established using two sunflower cultivars with significantly different levels of resistance to O. cumana. Transcriptome sequencing analysis of sunflower root material at the early intercropping stage clarified that the defense mechanism of the resistant cultivar (HZ2399) was a regular and stable process, while the defense response of the sensitive cultivar (SQ25) was transient and irregular. The resistance response of sunflower to O. cumana is similar to the resistance mechanism of phytopathogenic bacteria and is closely related to plant–pathogen interactions since both interactions involve upregulation of phenylpropanoid biosynthesis and plant hormone signal transduction pathways. According to transcriptomic analysis, early sunflower resistance to O. cumana parasitism was dependent on RPS4-mediated processes wherein EDS1 promoted the accumulation of the defense hormone salicylic acid, resulting in increased TGA3 expression. In addition, sulfate transporter proteins were highly upregulated and are likely to play an active role in sunflower resistance to O. cumana. The results of this study provide new insights into genes involved in sunflower resistance to O. cumana parasitism and form the basis for further research on plant–alloparasite interactions. This study may provide a useful reference for locating the master O. cumana resistance gene. Meanwhile, the subsequent study to determine whether these key genes have the function of resistance to O. cumana parasitism and to dissect the mechanism of their resistance to O. cumana parasitism will provide a theoretical basis for breeding varieties with resistance to O. cumana.

5. Conclusions

In this study, transcriptomic changes of O. cumana-resistant (HZ2399) and O. cumana-sensitive (SQ25) sunflower seedlings were investigated at six time points (0–72 h) following O. cumana infection. The process of resistance to O. cumana was similar in HZ2399 and SQ25 seedlings; however, significantly higher regulatory activity was observed in the resistant plants. The expression level of the three most significantly upregulated genes in HZ2399 (4CL2, EDS1, and TGA3) was significantly higher than that of SQ25, suggesting that they may be the main causes of O. cumana immunity in HZ2399. It is hypothesized that sunflower resistance to O. cumana parasitism is dependent on salicylic acid, a disease resistance protein (TIR-NBS-LRR class) family (RPS4), and EDS1.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/horticulturae8080701/s1, Figure S1: Plant pathogen interaction (ath04626); Table S1: Primer sequences for qPCR; Table S2: Statistics of sequencing data quality; Table S3: Alignment statistics results with reference genome for all sunflower samples; Table S4: KEGG pathway and GO function analysis of differential expression genes; Table S5: Gene expression data of qPCR in this study.

Author Contributions

Conceptualization, L.X. and W.Z.; experiments and data analysis, Q.H., L.Z. and Y.G.; writing—original draft preparation, Q.H.; writing—review and editing, Q.H. and Z.L. All authors have read and agreed to the published version of the manuscript.

Funding

This work was financially supported by the Project of Renovation Capacity Building for the Young Sci-Tech Talents Sponsored by Xinjiang Academy of Agricultural Sciences (No. xjnkq-2019005), the Special Project for the Construction of the Autonomous Region’s Innovation Environment (Talents, Bases) (No. 2019D01B27), and the Open Fund of Key Laboratory of Crop Pest Comprehensive Management in Northwest Desert Oasis of Ministry of Agriculture (No. KFJJ202005).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The transcriptome data have been uploaded to NCBI (https://www.ncbi.nlm.nih.gov/; accessed on 15 June 2022), and the accession number is SRP382412.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. O. cumana identification in field of SQ25. (a) Parasitic strength of SQ25 after sowing; (b) parasitic rate of SQ25 after sowing.
Figure 1. O. cumana identification in field of SQ25. (a) Parasitic strength of SQ25 after sowing; (b) parasitic rate of SQ25 after sowing.
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Figure 2. O. cumana parasitism of HZ2399 and SQ25 in the field. The picture on the left is HZ2399; the picture on the right is SQ25.
Figure 2. O. cumana parasitism of HZ2399 and SQ25 in the field. The picture on the left is HZ2399; the picture on the right is SQ25.
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Figure 3. The number of DEGs produced at 5 time points after inoculation with the control (0 h). K-up: upregulated genes in HZ2399; K-down: downregulated genes in HZ2399; M-up: upregulated genes in SQ25; M-down: downregulated genes in SQ25.
Figure 3. The number of DEGs produced at 5 time points after inoculation with the control (0 h). K-up: upregulated genes in HZ2399; K-down: downregulated genes in HZ2399; M-up: upregulated genes in SQ25; M-down: downregulated genes in SQ25.
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Figure 4. (a) Pearson correlation analysis of RNA-seq data from in HZ2399 and SQ25; (b) principal component analysis (PCA) plot showing clustering of transcriptomes of six time points after inoculation in HZ2399 and SQ25.
Figure 4. (a) Pearson correlation analysis of RNA-seq data from in HZ2399 and SQ25; (b) principal component analysis (PCA) plot showing clustering of transcriptomes of six time points after inoculation in HZ2399 and SQ25.
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Figure 5. Heatmap showing the expression profile of DEGs in different stages in both sunflowers cultivars. Color scale represents p-value.
Figure 5. Heatmap showing the expression profile of DEGs in different stages in both sunflowers cultivars. Color scale represents p-value.
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Figure 6. Gene expression patterns and enrichment of GO terms and KEGG pathways across six time points in HZ2399 and SQ25. (a) Gene expression patterns at six time points in HZ2399 and SQ25 predicted with STEM software. The number of genes and p-values for each pattern are indicated in the frame. (b) Results of the GO enrichment analysis of important processes in HZ2399 and SQ25. (c) Results of the KEGG pathway enrichment analysis of important processes in HZ2399 and SQ25. The p-value was used to indicate the significance of the most represented GO and KEGG Slim terms. Significant p-values are indicated in red or green, whereas nonsignificant p-values are indicated in light gray.
Figure 6. Gene expression patterns and enrichment of GO terms and KEGG pathways across six time points in HZ2399 and SQ25. (a) Gene expression patterns at six time points in HZ2399 and SQ25 predicted with STEM software. The number of genes and p-values for each pattern are indicated in the frame. (b) Results of the GO enrichment analysis of important processes in HZ2399 and SQ25. (c) Results of the KEGG pathway enrichment analysis of important processes in HZ2399 and SQ25. The p-value was used to indicate the significance of the most represented GO and KEGG Slim terms. Significant p-values are indicated in red or green, whereas nonsignificant p-values are indicated in light gray.
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Figure 7. Venn diagram of the percentage of transcription factors downregulated on HZ2399 compared to SQ25 at the same time point; Horizontal coordinate: transcription factors as a percentage of differential genes (%); vertical coordinate: A: KPA vs. MPA; AB: KPA vs. MPA, and KPB vs. MPB; ABC: KPA vs. MPA and KPB vs. MPB, and KPC vs. MPC; ABCD: KPA vs. MPA and KPB vs. MPB and KPC vs. MPC, and KPD vs. MPD; ABCDE: KPA vs. MPA and KPB vs. MPB and KPC vs. MPC, KPD vs. MPD, and KPE vs. MPE.
Figure 7. Venn diagram of the percentage of transcription factors downregulated on HZ2399 compared to SQ25 at the same time point; Horizontal coordinate: transcription factors as a percentage of differential genes (%); vertical coordinate: A: KPA vs. MPA; AB: KPA vs. MPA, and KPB vs. MPB; ABC: KPA vs. MPA and KPB vs. MPB, and KPC vs. MPC; ABCD: KPA vs. MPA and KPB vs. MPB and KPC vs. MPC, and KPD vs. MPD; ABCDE: KPA vs. MPA and KPB vs. MPB and KPC vs. MPC, KPD vs. MPD, and KPE vs. MPE.
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Figure 8. Analysis of the functional enrichment of the DEGs between HZ2399 and SQ25. (a) Venn diagrams of all time points exhibiting up- or downregulated expression between HZ2399 and SQ25. (b) Results of the KEGG pathway enrichment analysis of the DEGs between HZ2399 and SQ25. (c) Results of the GO enrichment analysis of the upDEGs between HZ2399 and SQ25.
Figure 8. Analysis of the functional enrichment of the DEGs between HZ2399 and SQ25. (a) Venn diagrams of all time points exhibiting up- or downregulated expression between HZ2399 and SQ25. (b) Results of the KEGG pathway enrichment analysis of the DEGs between HZ2399 and SQ25. (c) Results of the GO enrichment analysis of the upDEGs between HZ2399 and SQ25.
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Figure 9. Heatmap of the different pathways. (a) Heatmap of differentially expressed genes in plant–pathogen interaction signaling pathway; (b) heatmap of differentially expressed genes in phenylpropanoid biosynthesis pathway; (c) heatmap of differentially expressed genes in plant hormone signal transduction pathway.
Figure 9. Heatmap of the different pathways. (a) Heatmap of differentially expressed genes in plant–pathogen interaction signaling pathway; (b) heatmap of differentially expressed genes in phenylpropanoid biosynthesis pathway; (c) heatmap of differentially expressed genes in plant hormone signal transduction pathway.
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Figure 10. qPCR analysis of 15 important genes. (a) Analysis of the expression trend of the key genes in the phenylpropanoid biosynthesis pathway (PAL, 4CL2, CCR, POD10, and POD11) in HZ2399 and SQ25 and binary heatmap. (b) Analysis of the expression trend of the key genes in the plant–pathogen interaction pathway (EDSI, PTI1, WRKY2, RBOHE, and HSP83) in HZ2399 and SQ25 and binary heatmap. (c) Analysis of the expression trend of the key genes in the plant hormone signal transduction pathway (JAZ, TGA3, PP2C, PYL4, and ETR1) in HZ2399 and SQ25 and binary heatmap. Group1: 1 < n ≤ 5; Group2: 5 < n ≤ 15; Group3: 15 < n ≤ 25; Group4: n > 25.
Figure 10. qPCR analysis of 15 important genes. (a) Analysis of the expression trend of the key genes in the phenylpropanoid biosynthesis pathway (PAL, 4CL2, CCR, POD10, and POD11) in HZ2399 and SQ25 and binary heatmap. (b) Analysis of the expression trend of the key genes in the plant–pathogen interaction pathway (EDSI, PTI1, WRKY2, RBOHE, and HSP83) in HZ2399 and SQ25 and binary heatmap. (c) Analysis of the expression trend of the key genes in the plant hormone signal transduction pathway (JAZ, TGA3, PP2C, PYL4, and ETR1) in HZ2399 and SQ25 and binary heatmap. Group1: 1 < n ≤ 5; Group2: 5 < n ≤ 15; Group3: 15 < n ≤ 25; Group4: n > 25.
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Figure 11. Overview figure of the results in the present study.
Figure 11. Overview figure of the results in the present study.
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Table
Table 1. Sample list of RNA-seq.
Table 1. Sample list of RNA-seq.
Test NumberName of MaterialSampling TimeNumber of Replication
KC1HZ2399 ♀Interaction with O. cumana for 0 hReplication Ⅰ
KC2HZ2399 ♀Interaction with O. cumana for 0 hReplication Ⅱ
KC3HZ2399 ♀Interaction with O. cumana for 0 hReplication Ⅲ
KPA1HZ2399 ♀Interaction with O. cumana for 4 hReplication Ⅰ
KPA2HZ2399 ♀Interaction with O. cumana for 4 hReplication Ⅱ
KPA3HZ2399 ♀Interaction with O. cumana for 4 hReplication Ⅲ
KPB1HZ2399 ♀Interaction with O. cumana for 12 hReplication Ⅰ
KPB2HZ2399 ♀Interaction with O. cumana for 12 hReplication Ⅱ
KPB3HZ2399 ♀Interaction with O. cumana for 12 hReplication Ⅲ
KPC1HZ2399 ♀Interaction with O. cumana for 24 hReplication Ⅰ
KPC2HZ2399 ♀Interaction with O. cumana for 24 hReplication Ⅱ
KPC3HZ2399 ♀Interaction with O. cumana for 24 hReplication Ⅲ
KPD1HZ2399 ♀Interaction with O. cumana for 48 hReplication Ⅰ
KPD2HZ2399 ♀Interaction with O. cumana for 48 hReplication Ⅱ
KPD3HZ2399 ♀Interaction with O. cumana for 48 hReplication Ⅲ
KPE1HZ2399 ♀Interaction with O. cumana for 72 hReplication Ⅰ
KPE2HZ2399 ♀Interaction with O. cumana for 72 hReplication Ⅱ
KPE3HZ2399 ♀Interaction with O. cumana for 72 hReplication Ⅲ
GC1SQ25Interaction with O. cumana for 0 hReplication Ⅰ
GC2SQ25Interaction with O. cumana for 0 hReplication Ⅱ
GC3SQ25Interaction with O. cumana for 0 hReplication Ⅲ
MPA1SQ25Interaction with O. cumana for 4 hReplication Ⅰ
MPA2SQ25Interaction with O. cumana for 4 hReplication Ⅱ
MPA3SQ25Interaction with O. cumana for 4 hReplication Ⅲ
MPB1SQ25Interaction with O. cumana for 12 hReplication Ⅰ
MPB2SQ25Interaction with O. cumana for 12 hReplication Ⅱ
MPB3SQ25Interaction with O. cumana for 12 hReplication Ⅲ
MPC1SQ25Interaction with O. cumana for 24 hReplication Ⅰ
MPC2SQ25Interaction with O. cumana for 24 hReplication Ⅱ
MPC3SQ25Interaction with O. cumana for 24 hReplication Ⅲ
MPD1SQ25Interaction with O. cumana for 48 hReplication Ⅰ
MPD2SQ25Interaction with O. cumana for 48 hReplication Ⅱ
MPD3SQ25Interaction with O. cumana for 48 hReplication Ⅲ
MPE1SQ25Interaction with O. cumana for 72 hReplication Ⅰ
MPE2SQ25Interaction with O. cumana for 72 hReplication Ⅱ
MPE3SQ25Interaction with O. cumana for 72 hReplication Ⅲ
♀ indicates mother plant material.
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Huang, Q.; Lei, Z.; Xiang, L.; Zhang, W.; Zhang, L.; Gao, Y. Transcriptomic Analysis of Sunflower (Helianthus annuus) Roots Resistance to Orobanche cumana at the Seedling Stage. Horticulturae 2022, 8, 701. https://doi.org/10.3390/horticulturae8080701

AMA Style

Huang Q, Lei Z, Xiang L, Zhang W, Zhang L, Gao Y. Transcriptomic Analysis of Sunflower (Helianthus annuus) Roots Resistance to Orobanche cumana at the Seedling Stage. Horticulturae. 2022; 8(8):701. https://doi.org/10.3390/horticulturae8080701

Chicago/Turabian Style

Huang, Qixiu, Zhonghua Lei, Lijun Xiang, Wangfeng Zhang, Li Zhang, and Yan Gao. 2022. "Transcriptomic Analysis of Sunflower (Helianthus annuus) Roots Resistance to Orobanche cumana at the Seedling Stage" Horticulturae 8, no. 8: 701. https://doi.org/10.3390/horticulturae8080701

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